This document is the summary of the Introduction to R workshop.
All correspondence related to this document should be addressed to:
Omid Ghasemi (Macquarie University, Sydney, NSW, 2109, AUSTRALIA)
Email: omidreza.ghasemi@hdr.mq.edu.auThe aim of the study is to test if simple arguments are more effective in belief revision than more complex arguments. To that end, we present participants with an imaginary scenario (two alien creatures on a planet) and a theory (one creature is predator and the other one is prey) and we ask them to rate the likelihood truth of the theory based on a simple fact (We adapted this method from Gregg et al.,2017; see the original study here). Then, in a between-subject manipulation, participants will be presented with either 6 simple arguments (Modus Ponens conditionals) or 6 more complex arguments (Modus Tollens conditionals), and they will be asked to rate the likelihood truth of the initial theory on 7 stages.
The first stage is the base rating stage. The next three stages include supportive arguments of the theory and the last three arguments include disproving arguments of the theory. We hypothesized that the group with simple arguments shows better persuasion (as it reflects in higher ratings for the supportive arguments) and better dissuasion (as it reflects in lower ratings for the opposing arguments).
In the last part of the study, participants will be asked to answer several cognitive capacity/style measures including thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales. We hypothesized that cognitive ability, cognitive style, and open-mindedness are positive predictors of persuasion and dissuasion. These associations should be more pronounced for participants in the group with complex arguments because the ability and willingness to engage in deliberative thinking may favor participants to assess the underlying logical structure of those arguments. However, for participants in the simple group, the logical structure of arguments is more evident, so participants with lower ability can still assess the logical status of those arguments.
Thus, our hypotheses for this experiment are as follows:
Participants in the group with simple arguments have higher ratings for supportive arguments (They are more easily persuaded than those in the group with complex arguments).
Participants in the group with simple arguments have lower ratings for opposing arguments (They are more easily dissuaded than those in the group with complex arguments).
There are significant associations between thinking style (CRT), open-mindedness (AOT-E), reasoning ability (mindware), and numeracy scales with both persuasion and dissuasion indexes in each group and in the entire sample. The relationship between these measures should be stronger, although not significantly, for participants in the group with complex arguments.
First, we need to design the experiment. For this experiment, we use online platforms for data collection. There are several options such as Gorilla, JSpsych, Qualtrics, psychoJS (pavlovia), etc. Since we do not need any reaction time data, we simply use Qualtrics. For an overview of different lab-based and online platforms, see here.
Next, we need to decide on the number of participants (sample size). For this study, we do not sue power analysis since we cannot access more than 120 participants. However, it is highly suggested calculate sample size using power estimation. You can find some nice tutorials on how to do that here, here, and here.
After we created the experiment and decided on the sample size, the next step is to preresigter the study. However, it would be better to do a pilot with 4 or 5 participants, clean all the data, do the desired analysis, and then pre-register the analysis and those codes. You can find the preregistration form for the current study here.
Finally, we need to restructure our project in a tidy folder with different sub-folders. Having a clean and tidy folder structure can save us! There are different formats of folder structure (for example, see here and here), but for now, we use the following structure:
# load libraries
library(tidyverse)
library(here)
library(janitor)
library(broom)
library(afex)
library(emmeans)
library(knitr)
library(kableExtra)
library(ggsci)
library(patchwork)
library(skimr)
# install.packages("devtools")
# devtools::install_github("easystats/correlation")
library("correlation")
options(scipen=999) # turn off scientific notations
options(contrasts = c('contr.sum','contr.poly')) # set the contrast sum globally
options(knitr.kable.NA = '')
Artwork by Allison Horst: https://github.com/allisonhorst/stats-illustrations
R can be used as a calculator. For mathematical purposes, be careful of the order in which R executes the commands.
10 + 10
## [1] 20
4 ^ 2
## [1] 16
(250 / 500) * 100
## [1] 50
R is a bit flexible with spacing (but no spacing in the name of variables and words)
10+10
## [1] 20
10 + 10
## [1] 20
R can sometimes tell that you’re not finished yet
10 +
How to create a variable? Variable assignment using <- and =. Note that R is case sensitive for everything
pay <- 250
month = 12
pay * month
## [1] 3000
salary <- pay * month
Few points in naming variables and vectors: use short, informative words, keep same method (e.g., not using capital words, use only _ or . ).
Function is a set of statements combined together to perform a specific task. When we use a block of code repeatedly, we can convert it to a function. To write a function, first, you need to define it:
my_multiplier <- function(a,b){
result = a * b
return (result)
}
This code do nothing. To get a result, you need to call it:
my_multiplier (2,4)
## [1] 8
Fortunately, you do not need to write everything from scratch. R has lots of built-in functions that you can use:
round(54.6787)
## [1] 55
round(54.5787, digits = 2)
## [1] 54.58
Use ? before the function name to get some help. For example, ?round. You will see many functions in the rest of the workshop.
function class() is used to show what is the type of a variable.
TRUE, FALSE can be abbreviated as T, F. They has to be capital, ‘true’ is not a logical data:class(TRUE)
## [1] "logical"
class(F)
## [1] "logical"
class(2)
## [1] "numeric"
class(13.46)
## [1] "numeric"
class("ha ha ha ha")
## [1] "character"
class("56.6")
## [1] "character"
class("TRUE")
## [1] "character"
Can we change the type of data in a variable? Yes, you need to use the function as.---()
as.numeric(TRUE)
## [1] 1
as.character(4)
## [1] "4"
as.numeric("4.5")
## [1] 4.5
as.numeric("Hello")
## Warning: NAs introduced by coercion
## [1] NA
Vector: when there are more than one number or letter stored. Use the combine function c() for that.
sale <- c(1, 2, 3,4, 5, 6, 7, 8, 9, 10) # also sale <- c(1:10)
sale <- c(1:10)
sale * sale
## [1] 1 4 9 16 25 36 49 64 81 100
Subsetting a vector:
days <- c("Saturday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
days[2]
## [1] "Sunday"
days[-2]
## [1] "Saturday" "Monday" "Tuesday" "Wednesday" "Thursday" "Friday"
days[c(2, 3, 4)]
## [1] "Sunday" "Monday" "Tuesday"
Create a vector named my_vector with numbers from 0 to 1000 in it:
my_vector <- (0:1000)
mean(my_vector)
## [1] 500
median(my_vector)
## [1] 500
min(my_vector)
## [1] 0
range(my_vector)
## [1] 0 1000
class(my_vector)
## [1] "integer"
sum(my_vector)
## [1] 500500
sd(my_vector)
## [1] 289.1081
List: allows you to gather a variety of objects under one name (that is, the name of the list) in an ordered way. These objects can be matrices, vectors, data frames, even other list.
my_list = list(sale, 1, 3, 4:7, "HELLO", "hello", FALSE)
my_list
## [[1]]
## [1] 1 2 3 4 5 6 7 8 9 10
##
## [[2]]
## [1] 1
##
## [[3]]
## [1] 3
##
## [[4]]
## [1] 4 5 6 7
##
## [[5]]
## [1] "HELLO"
##
## [[6]]
## [1] "hello"
##
## [[7]]
## [1] FALSE
Factor: Factors store the vector along with the distinct values of the elements in the vector as labels. The labels are always character irrespective of whether it is numeric or character. For example, variable gender with “male” and “female” entries:
gender <- c("male", "male", "male", " female", "female", "female")
gender <- factor(gender)
R now treats gender as a nominal (categorical) variable: 1=female, 2=male internally (alphabetically).
summary(gender)
## female female male
## 1 2 3
Question: why when we ran the above function i.e. summary(), it showed three and not two levels of the data? Hint: run ‘gender’.
gender
## [1] male male male female female female
## Levels: female female male
So, be careful of spaces!
Create a gender factor with 30 male and 40 females (Hint: use the rep() function):
gender <- c(rep("male",30), rep("female", 40))
gender <- factor(gender)
gender
## [1] male male male male male male male male male male
## [11] male male male male male male male male male male
## [21] male male male male male male male male male male
## [31] female female female female female female female female female female
## [41] female female female female female female female female female female
## [51] female female female female female female female female female female
## [61] female female female female female female female female female female
## Levels: female male
There are two types of categorical variables: nominal and ordinal. How to create ordered factors (when the variable is nominal and values can be ordered)? We should add two additional arguments to the factor() function: ordered = TRUE, and levels = c("level1", "level2"). For example, we have a vector that shows participants’ education level.
edu<-c(3,2,3,4,1,2,2,3,4)
education<-factor(edu, ordered = TRUE)
levels(education) <- c("Primary school","high school","College","Uni graduated")
education
## [1] College high school College Uni graduated
## [5] Primary school high school high school College
## [9] Uni graduated
## Levels: Primary school < high school < College < Uni graduated
We have a factor with patient and control values. Here, the first level is control and the second level is patient. Change the order of levels, so patient would be the first level:
health_status <- factor(c(rep('patient',5),rep('control',5)))
health_status
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: control patient
health_status_reordered <- factor(health_status, levels = c('patient','control'))
health_status_reordered
## [1] patient patient patient patient patient control control control
## [9] control control
## Levels: patient control
Finally, can you relabel both levels to uppercase characters? (Hint: check ?factor)
health_status_relabeled <- factor(health_status, levels = c('patient','control'), labels = c('Patient','Control'))
health_status_relabeled
## [1] Patient Patient Patient Patient Patient Control Control Control
## [9] Control Control
## Levels: Patient Control
Matrices: All columns in a matrix must have the same mode(numeric, character, etc.) and the same length. It can be created using a vector input to the matrix function.
my_matrix = matrix(c(1,2,3,4,5,6,7,8,9), nrow = 3, ncol = 3)
my_matrix
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
Data frames: (two-dimensional objects) can hold numeric, character or logical values. Within a column all elements have the same data type, but different columns can be of different data type. Let’s create a dataframe:
id <- 1:200
group <- c(rep("Psychotherapy", 100), rep("Medication", 100))
response <- c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5))
my_dataframe <-data.frame(Patient = id,
Treatment = group,
Response = response)
We also could have done the below
my_dataframe <-data.frame(Patient = c(1:200),
Treatment = c(rep("Psychotherapy", 100), rep("Medication", 100)),
Response = c(rnorm(100, mean = 30, sd = 5),
rnorm(100, mean = 25, sd = 5)))
In large data sets, the function head() enables you to show the first observations of a data frames. Similarly, the function tail() prints out the last observations in your data set.
head(my_dataframe)
tail(my_dataframe)
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 34.70092 |
| 2 | Psychotherapy | 41.88797 |
| 3 | Psychotherapy | 25.08800 |
| 4 | Psychotherapy | 33.26310 |
| 5 | Psychotherapy | 31.60971 |
| 6 | Psychotherapy | 23.99579 |
| Patient | Treatment | Response | |
|---|---|---|---|
| 195 | 195 | Medication | 22.22749 |
| 196 | 196 | Medication | 26.01247 |
| 197 | 197 | Medication | 30.17011 |
| 198 | 198 | Medication | 22.59494 |
| 199 | 199 | Medication | 29.29573 |
| 200 | 200 | Medication | 26.04027 |
Similar to vectors and matrices, brackets [] are used to selects data from rows and columns in data.frames:
my_dataframe[35, 3]
## [1] 29.23672
How can we get all columns, but only for the first 10 participants?
my_dataframe[1:10, ]
| Patient | Treatment | Response |
|---|---|---|
| 1 | Psychotherapy | 34.70092 |
| 2 | Psychotherapy | 41.88797 |
| 3 | Psychotherapy | 25.08800 |
| 4 | Psychotherapy | 33.26310 |
| 5 | Psychotherapy | 31.60971 |
| 6 | Psychotherapy | 23.99579 |
| 7 | Psychotherapy | 30.36041 |
| 8 | Psychotherapy | 36.53295 |
| 9 | Psychotherapy | 28.17176 |
| 10 | Psychotherapy | 30.93721 |
How to get only the Response column for all participants?
my_dataframe[ , 3]
## [1] 34.70092 41.88797 25.08800 33.26310 31.60971 23.99579 30.36041
## [8] 36.53295 28.17176 30.93721 30.92142 38.82162 34.95608 33.78289
## [15] 31.88302 23.98487 32.68682 34.24377 26.14660 23.97673 27.78229
## [22] 30.64321 30.23933 33.35966 37.52429 31.95826 36.68059 30.90489
## [29] 37.50605 30.00930 27.77501 30.93168 29.62510 28.90410 29.23672
## [36] 19.71450 36.45978 42.75902 35.29394 28.78575 27.28674 31.11260
## [43] 30.35200 30.17337 29.70474 37.36315 37.85181 26.18070 22.46728
## [50] 20.98786 21.03127 31.18289 38.03898 33.04688 24.15885 40.13446
## [57] 24.91924 21.37063 37.34523 16.65720 43.27116 28.75091 21.52645
## [64] 27.22263 30.45472 36.87326 31.66936 28.75838 30.19378 22.61372
## [71] 39.30732 31.91143 29.46254 34.18536 28.93321 39.27136 26.77799
## [78] 37.06550 26.80027 28.38800 29.67149 28.68350 28.27039 23.16943
## [85] 34.55464 26.39095 22.83425 27.70849 34.12862 25.99384 33.75201
## [92] 30.40326 39.22291 17.82195 21.73108 30.36474 32.27294 25.91730
## [99] 38.25061 23.02575 33.26268 27.02132 25.06723 15.31837 20.32768
## [106] 28.64088 20.46997 22.36860 12.41242 28.72662 21.63816 33.31252
## [113] 24.13434 31.63015 24.83955 31.39216 22.59675 30.06282 22.85433
## [120] 31.03004 26.33620 20.87316 30.45489 33.65588 20.39493 26.50646
## [127] 15.60010 29.82494 28.72798 26.47358 23.24629 17.42689 34.86608
## [134] 18.76335 24.14146 17.77577 29.89969 27.97703 21.10715 22.32984
## [141] 26.00448 25.83052 21.32632 35.88054 31.72849 21.08781 31.07852
## [148] 28.72310 27.50717 18.73048 31.32646 26.68313 15.87932 22.78849
## [155] 14.61576 27.78017 15.01873 27.97408 24.97825 23.41314 15.38537
## [162] 28.25309 21.69138 28.28512 27.71101 35.10529 25.53642 21.64751
## [169] 26.12292 34.16566 20.20791 29.58093 21.16437 27.11175 26.59794
## [176] 18.82553 20.86012 28.93838 22.24265 23.54466 28.71413 30.29684
## [183] 27.90121 21.13579 26.59486 26.54909 18.04762 23.35257 22.58064
## [190] 30.80603 22.76379 26.11364 29.48092 22.20632 22.22749 26.01247
## [197] 30.17011 22.59494 29.29573 26.04027
Another easier way for selecting particular items is using their names that is more helpful than number of the rows in large data sets:
my_dataframe[ , "Response"]
# OR:
my_dataframe$Response